Video stream encryption and decryption method and system based on artificial intelligence

Abstract

The invention discloses a video stream encryption and decryption method and system based on artificial intelligence. The method comprises the following steps: preprocessing an input video stream, removing noise and adjusting the resolution; extracting image and time sequence features of the video stream by using an artificial intelligence algorithm; dynamically generating an encryption strategy according to the extracted features, and selecting a proper encryption algorithm and key parameters; encrypting the video stream according to the encryption strategy and transmitting the encrypted video stream through a network; and the receiving end recovers the original video content by using a corresponding decryption algorithm according to the encryption strategy. The dynamic adjustment of the encryption strategy is realized through the artificial intelligence technology, the attack can be effectively resisted, and the security of the video stream is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial intelligence, in particular to a video stream encryption and decryption method and system based on artificial intelligence. BACKGROUND

[0002] With the rapid development of Internet technology, video streaming media is increasingly widely used in people's lives, such as online video conferencing, network live streaming, video on demand, etc. However, video streams face many security threats during transmission, such as data leakage, tampering, theft, etc., which not only infringes on the privacy of users, but also may cause huge economic losses to enterprises and institutions.

[0003] Traditional video stream encryption and decryption methods mainly rely on fixed encryption algorithms and key management mechanisms, which can guarantee the security of video streams to some extent, but their security and flexibility gradually reveal their shortcomings in the face of increasingly complex network attack methods. For example, fixed encryption algorithms are easy to crack, and once the key is leaked, the security of the entire video stream will be seriously threatened; moreover, traditional methods often have low encryption and decryption efficiency when processing large-scale video stream data, and are difficult to adapt to different network environments. SUMMARY

[0004] Therefore, the embodiments of the present application provide a video stream encryption and decryption method and system based on artificial intelligence, which solves the problem of low encryption and decryption efficiency and difficulty in adapting to different network environments in the prior art.

[0005] In a first aspect, a video stream encryption and decryption method based on artificial intelligence is provided, which includes:

[0006] Preprocessing the input video stream to remove noise and adjust the resolution;

[0007] Using an artificial intelligence algorithm to extract features from the preprocessed video stream, including image texture, color, shape features, and video stream time series features;

[0008] According to the extracted video stream features, an encryption strategy is dynamically generated through an artificial intelligence model, including selecting an encryption algorithm, determining a key generation method and length;

[0009] According to the generated encryption strategy, the video stream is encrypted, and the encrypted video stream is transmitted through the network;

[0010] After receiving the encrypted video stream, the receiver uses the corresponding decryption algorithm according to the encryption strategy to decrypt it and restore it to the original video content.

[0011] Optionally, in the preprocessing step, an adaptive filtering algorithm is used to remove noise in the video stream, and the adaptive filtering algorithm includes dynamically adjusting filtering parameters according to the noise type and intensity of the video stream.

[0012] According to the resolution and code rate of the video stream, the resolution is dynamically adjusted, specifically by analyzing the definition requirement and transmission bandwidth limitation of the video stream, selecting a suitable resolution adjustment strategy to optimize the subsequent processing efficiency, and ensuring smooth transmission of the video stream under different network environments.

[0013] Optionally, in the feature extraction step, a convolutional neural network is used to extract features from each frame of the video stream, and the extracted feature vector includes texture features, color distribution features and inter-frame motion change features of the image.

[0014] Specifically, the convolutional neural network model includes multiple convolutional layers, pooling layers and fully connected layers, which can automatically learn high-level feature representation of images by training a large number of labeled video frame data; time series features are extracted by a long short-term memory network, which is used to analyze the dynamic changes of the video stream, and the long short-term memory network model can capture the long-term dependence between frames in the video stream and extract the time series feature vector of the video stream.

[0015] Optionally, in the encryption strategy generation step, a machine learning model is used to analyze the relationship between video stream features and security requirements, specifically by constructing a decision tree model to dynamically select symmetric encryption algorithm or asymmetric encryption algorithm according to the importance and real-time requirements of the video stream; and the key length and encryption parameters are optimized by genetic algorithm, which simulates the natural selection process to iteratively optimize the key length and encryption parameters.

[0016] The encryption strategy also includes dynamically adjusting the encryption mode of the encryption algorithm according to the type and application scenario of the video stream, including electronic cipher block mode or cipher block chain mode.

[0017] Optionally, in the video stream encryption step, the encryption algorithm dynamically adjusts the encryption strength according to the network environment, specifically by monitoring the bandwidth, delay and packet loss rate parameters of the network to adjust the encryption strength of the encryption algorithm in real time.

[0018] During the encryption process, potential risk points are predicted by a neural network to adjust the encryption parameters in advance, ensuring the stability of the encryption process; specifically, a deep neural network is used to monitor the data in real time during the encryption process to predict possible encryption errors or security vulnerabilities, and adjust the encryption parameters in time; wherein the encryption parameters include key update frequency and encryption block size.

[0019] Optionally, in the video stream transmission step, an artificial intelligence algorithm is used to monitor the network environment in real time, including bandwidth, delay and packet loss rate parameters.

[0020] Specifically, through the sensor network deployed on the network node, the network state data is collected in real time, and the data analysis and prediction are performed by using the machine learning algorithm; according to the monitoring result, the encoding format and resolution of the video stream are dynamically adjusted, specifically, through the adaptive encoding technology, the appropriate encoding format is selected according to the network state, and the resolution of the video stream is adjusted, so as to ensure the stable transmission of the video stream under different network environments.

[0021] Meanwhile, through the data integrity checking mechanism, the lost or damaged data is retransmitted or repaired, specifically, the cyclic redundancy check and forward error correction encoding technology are adopted to check the integrity of the data in the transmission process, and once the data loss or damage is found, the retransmission or repair mechanism is started immediately to ensure the integrity and availability of the video stream.

[0022] Optionally, in the video stream decryption step, the decryption algorithm dynamically adjusts the decryption parameters according to the encryption strategy, specifically, through the parameter adaptive module in the decryption algorithm, the decryption parameters are dynamically adjusted according to the encryption parameters in the encryption strategy.

[0023] Through the machine learning model, the errors that may occur in the decryption process are predicted, and the correction measures are taken in advance; wherein, the deep learning model is used to monitor the data in the decryption process in real time, to predict the possible decryption errors, including key errors and data format errors, and the adaptive error correction mechanism is used to correct the errors in time.

[0024] In the second aspect, an electronic device is provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the video stream encryption and decryption method of any one of the above-mentioned first aspect when executing the computer program.

[0025] In the third aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the video stream encryption and decryption method of any one of the above-mentioned first aspect.

[0026] In the fourth aspect, a computer program product is provided, which stores a computer program, and the computer program is executed by a processor to implement the video stream encryption and decryption method of any one of the above-mentioned first aspect.

[0027] The technical scheme provided by the embodiments of the present application has at least the following beneficial effects:

[0028] (1) The encryption strategy is dynamically generated by the artificial intelligence algorithm, which can flexibly select the encryption algorithm and key parameters according to the characteristics and security requirements of the video stream. Compared with the traditional fixed encryption method, this method can effectively resist attacks on specific encryption algorithms, significantly improve the security of the video stream in the transmission process, and protect the privacy and data security of users.

[0029] (2) In the encryption and decryption process, the application utilizes artificial intelligence technology to monitor and analyze the network environment and video stream characteristics in real time, dynamically adjusts the encryption strength and decryption parameters. This adaptive mechanism can optimize the encryption and decryption process, reduce the consumption of computing resources, improve the transmission efficiency and decryption speed of video streams, and ensure smooth playback of video streams under the premise of ensuring security. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can be obtained from the provided drawings without creative labor.

[0031] Figure 1 A step flow chart of a video stream encryption and decryption method based on artificial intelligence provided for an embodiment of the present application;

[0032] Figure 2 A schematic diagram of an electronic device provided for an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0034] In the description of the present application, the terms "include", "have" and any variants thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to only those steps or units explicitly listed, but can also include other steps or units inherent to these processes, methods, products or devices, or steps or units added based on further optimization of the concept of the present application.

[0035] With the rapid development of Internet technology, video streaming media is increasingly widely used in people's lives, such as online video conferencing, network live streaming, video on demand, etc. However, video streams face many security threats in the transmission process, such as data leakage, tampering, theft, etc., which not only infringes on the privacy of users, but also may cause huge economic losses to enterprises and institutions.

[0036] Traditional video stream encryption and decryption methods mainly rely on fixed encryption algorithms and key management mechanisms, which can guarantee the security of video streams to a certain extent, but their security and flexibility gradually reveal their shortcomings in the face of increasingly complex network attack methods. For example, fixed encryption algorithms are easy to crack, and once the key is leaked, the security of the entire video stream will be seriously threatened; moreover, traditional methods often have low encryption and decryption efficiency when processing large-scale video stream data, and are difficult to adapt to different network environments.

[0037] Please refer to Figure 1 which shows a flowchart of a video stream encryption and decryption method based on artificial intelligence provided by an embodiment of the present application. The method can include the following steps:

[0038] S1, pre-processing the input video stream to remove noise and adjust the resolution.

[0039] In this step, the input video stream is first received, and the main task is to remove noise in the video stream and adjust its resolution to ensure the accuracy and efficiency of subsequent processing. The specific implementation is as follows:

[0040] An adaptive filtering algorithm is used to denoise the video stream. This algorithm can dynamically adjust the filtering parameters according to the noise type and intensity of the video stream. For example, for Gaussian noise, the algorithm adjusts the kernel size and standard deviation of the filter; for impulse noise, the algorithm switches to median filtering mode. This adaptability enables the denoising process to achieve optimal results for different types of noise.

[0041] According to the resolution and code rate of the video stream, the pre-processing module analyzes the clarity requirements of the video stream and the bandwidth limitations, and dynamically selects the appropriate resolution adjustment strategy. For example, for a high-resolution but bandwidth-limited video stream, the module will adjust its resolution from 4K to 1080p to optimize the efficiency of subsequent processing, while ensuring smooth transmission of the video stream in different network environments.

[0042] S2, using an artificial intelligence algorithm to extract features from the pre-processed video stream.

[0043] The extracted features include image texture, color, shape features, and video stream time series features; the main task in this step is to extract image texture, color, shape features, and video stream time series features from the video stream. The specific implementation is as follows:

[0044] Image feature extraction: Convolutional Neural Network (CNN) is used to extract features from each frame of the video stream. The CNN model contains multiple convolutional layers, pooling layers and fully connected layers. Through training a large number of labeled video frame data, CNN can automatically learn the high-level feature representation of the image, and the extracted feature vector includes the texture feature, color distribution feature and inter-frame motion change feature of the image.

[0045] Time series feature extraction: Time series features are extracted by Long Short-Term Memory Network (LSTM). LSTM model can capture the long-term dependence between frames in video stream, and extract time series feature vectors of video stream. These feature vectors provide comprehensive feature information for subsequent encryption strategy generation, which helps to generate more accurate encryption strategies.

[0046] S3, according to the extracted video stream features, the encryption strategy is dynamically generated by artificial intelligence model, including selecting encryption algorithm, determining key generation method and length.

[0047] In this step, the appropriate encryption algorithm is selected, the key generation method and length are determined. The specific implementation is as follows:

[0048] Machine learning models (such as decision tree models) are used to analyze the relationship between video stream features and security requirements. According to the importance and real-time requirements of video stream, symmetric encryption algorithm (such as AES) or asymmetric encryption algorithm (such as RSA) is dynamically selected. For example, for real-time video streams with high real-time requirements, AES algorithm is selected to ensure encryption efficiency; for video streams with extremely high security requirements, RSA algorithm is selected to ensure encryption strength.

[0049] Genetic algorithm is used to optimize key length and encryption parameters. Genetic algorithm simulates the process of natural selection to iteratively optimize key length and encryption parameters. For example, the initial population is composed of randomly generated key parameters, the security and efficiency of each parameter group are evaluated by the fitness function, the optimal parameters are selected for crossover and mutation operations, and finally the optimal key length and encryption parameters are obtained.

[0050] According to the type and application scenario of the video stream, the encryption mode of the encryption algorithm is dynamically adjusted. For example, for video streams that require high security, Cipher Block Chaining mode (CBC) is selected; for video streams that require high efficiency, Electronic Codebook mode (ECB) is selected.

[0051] S4, according to the generated encryption strategy, the video stream is encrypted, and the encrypted video stream is transmitted through the network.

[0052] The encryption module encrypts the video stream according to the generated encryption strategy, and transmits the encrypted video stream through the network. The main task of this module is to ensure the stability and security of the encryption process. The specific implementation is as follows:

[0053] The encryption algorithm dynamically adjusts the encryption strength according to the network environment. By monitoring the bandwidth, delay and packet loss rate parameters of the network, the encryption strength of the encryption algorithm is adjusted in real time. For example, in a high-bandwidth, low-delay network environment, the encryption strength can be appropriately increased; in a low-bandwidth, high-delay network environment, the encryption strength can be appropriately reduced to ensure encryption efficiency.

[0054] By using a neural network to predict potential risk points in the encryption process, encryption parameters are adjusted in advance. Specifically, a deep neural network is used to monitor data in real time during the encryption process to predict possible encryption errors or security vulnerabilities, and encryption parameters such as key update frequency and encryption block size are adjusted in a timely manner to ensure the stability of the encryption process.

[0055] The transmission module uses artificial intelligence algorithms to monitor the network environment in real time, including bandwidth, delay and packet loss rate parameters. The main task of this module is to ensure stable transmission of video streams in different network environments. The specific implementation is as follows:

[0056] Through the deployment of sensor networks on network nodes, network state data is collected in real time, and machine learning algorithms are used for data analysis and prediction. For example, a linear regression model is used to predict the trend of network bandwidth, and a decision tree model is used to predict network delay and packet loss rate.

[0057] According to the monitoring results, the encoding format and resolution of the video stream are dynamically adjusted. Specifically, through adaptive encoding technology, the appropriate encoding format (such as H.264, H.265) is selected according to the network state, and the resolution of the video stream is adjusted to ensure stable transmission of the video stream in different network environments.

[0058] Through a data integrity checking mechanism, lost or damaged data is retransmitted or repaired. Specifically, the cyclic redundancy check (CRC) and forward error correction coding (FEC) techniques are used to check the integrity of the data during transmission. Once data loss or damage is detected, the retransmission or repair mechanism is immediately activated to ensure the integrity and availability of the video stream.

[0059] S5, after receiving the encrypted video stream, the receiving end uses the corresponding decryption algorithm according to the encryption strategy to decrypt and restore the original video content.

[0060] In this step, the video stream is decrypted according to the encryption strategy using the corresponding decryption algorithm to restore the original video content. The main task of this module is to ensure the accuracy and efficiency of the decryption process. The specific implementation is as follows:

[0061] The decryption algorithm dynamically adjusts the decryption parameters according to the encryption strategy. Specifically, through the parameter adaptive module in the decryption algorithm, the decryption parameters are dynamically adjusted according to the encryption parameters in the encryption strategy. For example, according to the key length and encryption mode used during encryption, the parameters of the decryption algorithm are dynamically adjusted.

[0062] Predict errors that may occur during the decryption process through a machine learning model, and take corrective measures in advance. Specifically, a deep learning model is used to monitor the data in the decryption process in real time, predict possible decryption errors such as key errors and data format errors, and correct errors in a timely manner through an adaptive error correction mechanism to improve the accuracy and efficiency of decryption.

[0063] Through the above specific embodiments, the present application can realize efficient and secure encryption and decryption processing of video streams, and ensure stable transmission and accurate decryption of video streams in different network environments.

[0064] The present application also provides an artificial intelligence-based video stream encryption and decryption system. The system can include:

[0065] A preprocessing module for preprocessing the input video stream, removing noise and adjusting the resolution;

[0066] A feature extraction module for extracting features from the preprocessed video stream using artificial intelligence algorithms, including image texture, color, shape features, and video stream time series features;

[0067] An encryption module for dynamically generating an encryption strategy based on the extracted video stream features through an artificial intelligence model, including selecting an encryption algorithm, determining a key generation method and length;

[0068] A transmission module for encrypting the video stream according to the generated encryption strategy and transmitting the encrypted video stream over the network;

[0069] After receiving the encrypted video stream, the receiving end uses the corresponding decryption algorithm according to the encryption strategy to decrypt and restore the original video content.

[0070] For specific limitations of the artificial intelligence-based video stream encryption and decryption system, please refer to the limitations of the artificial intelligence-based video stream encryption and decryption method in the above, which will not be repeated here. The various modules in the above artificial intelligence-based video stream encryption and decryption system can be realized by software, hardware and their combinations. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0071] In one embodiment, an electronic device is provided, which may be a computer, and its internal structure diagram may be as follows: Figure 2 As shown, the electronic device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for encrypting and decrypting video stream data based on artificial intelligence. The network interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an artificial intelligence-based video stream encryption and decryption method.

[0072] Those skilled in the art will understand that, Figure 2 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0073] In one embodiment of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described artificial intelligence-based video stream encryption and decryption method.

[0074] In one embodiment of this application, a computer program product is provided, including a computer program / instructions, which, when executed by a processor, implements the steps of the above-described AI-based video stream encryption and decryption method.

[0075] The computer-readable storage medium and computer program product provided in this embodiment are similar in implementation principle and technical effect to the above method embodiments, and will not be repeated here.

[0076] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, databases, or other media in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (SyMchliMk) DRAM (SLDRAM), memory bus (RaMbus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0077] The technical features of the above-mentioned embodiments can be combined in any way. In order to make the description simple, all possible combinations of the technical features in the above-mentioned embodiments are not described, but as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0078] The above-mentioned embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of the patent protection of the present application should be subject to the appended claims.

Claims

1. An artificial intelligence-based video stream encryption and decryption method, characterized by, The method comprises: preprocessing the input video stream to remove noise and adjust the resolution; using an artificial intelligence algorithm to extract features from the preprocessed video stream, including image texture, color, shape features, and video stream time series features; According to the extracted video stream features, the encryption strategy is dynamically generated through the artificial intelligence model, including selecting the encryption algorithm, determining the key generation method and length; According to the generated encryption strategy, the video stream is encrypted, and the encrypted video stream is transmitted through the network; After receiving the encrypted video stream, the receiving end uses the corresponding decryption algorithm according to the encryption strategy to restore the original video content.

2. The video stream encryption / decryption method of claim 1, wherein, In the preprocessing step, an adaptive filtering algorithm is used to remove noise from the video stream, and the adaptive filtering algorithm includes dynamically adjusting the filtering parameters according to the noise type and intensity of the video stream; According to the resolution and code rate of the video stream, the resolution is dynamically adjusted, specifically by analyzing the clarity requirement and transmission bandwidth limitation of the video stream, selecting the appropriate resolution adjustment strategy to optimize the subsequent processing efficiency, and ensuring the smooth transmission of the video stream in different network environments.

3. The video stream encryption / decryption method of claim 1, wherein, In the feature extraction step, a convolutional neural network is used to extract features from each frame of the video stream, and the extracted feature vector includes image texture features, color distribution features, and inter-frame motion change features; Specifically, the convolutional neural network model contains multiple convolutional layers, pooling layers and fully connected layers, which can automatically learn the high-level feature representation of the image through training a large number of labeled video frame data; The time series feature is extracted by a long short-term memory network, which is used to analyze the dynamic change of the video stream, and the long short-term memory network model can capture the long-term dependence between frames in the video stream and extract the time series feature vector of the video stream.

4. The video stream encryption / decryption method of claim 1, wherein, In the encryption strategy generation step, a machine learning model is used to analyze the relationship between video stream features and security requirements, specifically by constructing a decision tree model to dynamically select symmetric encryption algorithm or asymmetric encryption algorithm according to the importance and real-time requirement of the video stream; and the key length and encryption parameters are optimized by genetic algorithm, which simulates the natural selection process to iteratively optimize the key length and encryption parameters; The encryption strategy also includes dynamically adjusting the encryption mode of the encryption algorithm according to the type and application scenario of the video stream, including electronic cipher block mode or cipher block chain mode.

5. The video stream encryption / decryption method of claim 1, wherein, In the video stream encryption step, the encryption algorithm dynamically adjusts the encryption strength according to the network environment, specifically by monitoring the bandwidth, delay and packet loss rate parameters of the network to adjust the encryption strength of the encryption algorithm in real time; In the encryption process, the neural network predicts potential risk points in advance to adjust the encryption parameters, ensuring the stability of the encryption process; Specifically, a deep neural network is used to monitor the data in the encryption process in real time to predict possible encryption errors or security vulnerabilities and adjust the encryption parameters in a timely manner; wherein the encryption parameters include key update frequency and encryption block size.

6. The video stream encryption / decryption method of claim 1, wherein, In the video stream transmission step, an artificial intelligence algorithm is used to monitor the network environment in real time, including bandwidth, delay and packet loss rate parameters; Specifically, through the sensor network deployed on the network node, real-time collection of network state data is performed, and data analysis and prediction are performed by using a machine learning algorithm; according to the monitoring result, the encoding format and resolution of the video stream are dynamically adjusted, specifically, through an adaptive encoding technology, a suitable encoding format is dynamically selected according to the network state, and the resolution of the video stream is adjusted, so as to ensure stable transmission of the video stream under different network environments; At the same time, through a data integrity checking mechanism, retransmission or repair is performed on lost or damaged data, specifically, by using a cyclic redundancy check and a forward error correction encoding technology, integrity checking is performed on the data in the transmission process, once data loss or damage is found, the retransmission or repair mechanism is immediately started, and the integrity and availability of the video stream are ensured.

7. The video stream encryption / decryption method of claim 1, wherein, In the video stream decryption step, the decryption algorithm dynamically adjusts the decryption parameters according to the encryption strategy, specifically, through a parameter adaptive module in the decryption algorithm, the decryption parameters are dynamically adjusted according to the encryption parameters in the encryption strategy; Through a machine learning model, errors that may occur in the decryption process are predicted, and correction measures are taken in advance; wherein, a deep learning model is used to monitor the data in the decryption process in real time, to predict possible decryption errors, including key errors and data format errors, and an adaptive error correction mechanism is used to correct the errors in time.

8. An electronic device, comprising: The computer program is stored in the memory and is executed by the processor to implement the video stream encryption and decryption method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer program is stored in the memory and is executed by the processor to implement the video stream encryption and decryption method according to any one of claims 1 to 6.

10. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instruction is executed by the processor to implement the video stream encryption and decryption method according to any one of claims 1 to 6.

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